@inproceedings{4bf2d33e3cad4344b895628c76f7bfa8,
title = "Using Retrieval-Augmented Generation to Capture Molecularly-Driven Treatment Relationships for Precision Oncology",
abstract = "Modern generative artificial intelligence techniques like retrieval-augmented generation (RAG) may be applied in support of precision oncology treatment discussions. Experts routinely review published literature for evidence and recommendations of treatments in a labor-intensive process. A RAG pipeline may help reduce this effort by providing chunks of text from these publications to an off-the-shelf large language model (LLM), allowing it to answer related questions without any fine-tuning. This potential application is demonstrated by retrieving treatment relationships from a trusted data source (OncoKB) and reproducing over 80% of them by asking simple questions to an untrained Llama 2 model with access to relevant abstracts.",
keywords = "Large Language Models, Precision Oncology, Retrieval-Augmented Generation",
author = "Kory Kreimeyer and Canzoniero, {Jenna V.} and Maria Fatteh and Valsamo Anagnostou and Taxiarchis Botsis",
note = "Publisher Copyright: {\textcopyright} 2024 The Authors.; 34th Medical Informatics Europe Conference, MIE 2024 ; Conference date: 25-08-2024 Through 29-08-2024",
year = "2024",
month = aug,
day = "22",
doi = "10.3233/SHTI240575",
language = "English (US)",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "983--987",
editor = "John Mantas and Arie Hasman and George Demiris and Kaija Saranto and Michael Marschollek and Arvanitis, {Theodoros N.} and Ivana Ognjanovic and Arriel Benis and Parisis Gallos and Emmanouil Zoulias and Elisavet Andrikopoulou",
booktitle = "Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024",
address = "Netherlands",
}